Packages
Case studies
Load data
For one lemma
Comparative analyses
Usage intensity
uses <- get_uses(tweets)
uses_tot <- get_uses_tot(uses)
age = get_age(uses)
coef_var <- get_coef_var(uses)
mean_date <- get_mean_date(uses)
max_date <- get_max_date(uses)
uses_month <- conv_uses_month(uses)
uses_plt <- plt_uses(uses_month, lemma, mean_date, max_date)
ggplotly(uses_plt)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Advanced (S-curve)
Candidates
- ghosting
- deep learning
- artificial intelligence
- co-working
- climate emergency
Degree centralization
Diachronic
df_comp %>%
filter(LEMMA ==lemma) %>%
select(SUBSET, CENT_DEGREE) %>%
mutate(SUBSET = factor(SUBSET, levels=c('first', 'mean', 'max', 'last', 'full'))) %>%
ggplot(., aes(x=SUBSET, y=CENT_DEGREE, group=1)) +
geom_point() +
geom_line()

Comparative analyses
Processing status
Lemma list
df_comp %>%
select(LEMMA, SUBSET, STAMP) %>%
filter(SUBSET == 'full') %>%
mutate(STAMP = as_datetime(STAMP)) %>%
arrange(desc(STAMP))
Dataset statistics
df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, SUBSET, USES, USERS) %>%
dplyr::summarise(
USES_TOT = sum(USES),
USERS_TOT = sum(USERS)
)
Degree centrality
Overall
List
df_comp %>%
select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
filter(
SUBSET == 'full',
# USES >= 10000
) %>%
arrange((CENT_DEGREE))
Plot
plt <- df_comp %>%
select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
filter(SUBSET == 'full') %>%
arrange((CENT_DEGREE)) %>%
ggplot(., aes(x=CENT_DEGREE, y=reorder(LEMMA, CENT_DEGREE))) +
geom_point() +
scale_x_continuous(trans='log')
ggplotly(plt)
Over time
Across all lemmas
df_comp %>%
filter(
SUBSET != 'full',
EDGES >= 100
) %>%
group_by(SUBSET) %>%
summarize(CENT_AVG = mean(CENT_DEGREE)) %>%
mutate(SUBSET = factor(SUBSET, levels=c('first', 'mean', 'max', 'last'))) %>%
ggplot(., aes(x=SUBSET, y=CENT_AVG, group=1)) +
geom_point() +
geom_line()

Biggest changes
df_comp %>%
select(LEMMA, SUBSET, CENT_DEGREE, EDGES) %>%
filter(
SUBSET %in% c('first', 'last'),
EDGES >= 100
) %>%
dplyr::group_by(LEMMA) %>%
dplyr::mutate(CENT_DIFF = lag(CENT_DEGREE) - CENT_DEGREE) %>%
drop_na() %>%
select(-SUBSET) %>%
rename(CENT_LAST = CENT_DEGREE) %>%
arrange(desc(CENT_DIFF))
Usage intensity vs. network characteristics
Uses vs. degree centralization
Plot
plt <- df_comp %>%
filter(
SUBSET == 'full',
# USES >= 1000
) %>%
select(LEMMA, CENT_DEGREE, USES, EDGES) %>%
ggplot(., aes(x=CENT_DEGREE, y=USES)) +
geom_text(aes(label=LEMMA)) +
scale_y_continuous(trans='log') +
scale_x_continuous(trans='log') +
geom_smooth(method=lm)
ggplotly(plt)
Correlation
df_corr_full <- df_comp %>%
filter(
SUBSET != 'full',
EDGES >= 100
) %>%
select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
cor.test(df_corr_full$USES, df_corr_full$CENT_DEGREE)
Degree centrality vs. communities
Correlation
df_comp %>%
filter(SUBSET == 'last') %>%
select(CENT_DEGREE, COMMUNITIES) %>%
mutate(COMMUNITIES = as.numeric(COMMUNITIES)) %>%
correlate()
Plot
df_comp %>%
filter(SUBSET == 'last') %>%
select(LEMMA, CENT_DEGREE, COMMUNITIES) %>%
ggplot(., aes(x=CENT_DEGREE, y=as.numeric(COMMUNITIES))) +
geom_text(aes(label=LEMMA)) +
scale_x_continuous(trans='log')
Uses vs. users
Plot
plt <- df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, USES, USERS) %>%
ggplot(., aes(x=USERS, y=USES)) +
geom_text(aes(label=LEMMA)) +
scale_x_continuous(trans='log') +
scale_y_continuous(trans='log') +
geom_smooth(method=lm)
ggplotly(plt)
Correlation
df_comp %>%
filter(SUBSET == 'full') %>%
select(USES, USERS) %>%
correlate()
Coefficient of variation
df_comp %>%
filter(
SUBSET == 'full',
USES >= 1000
) %>%
select(LEMMA, USES, COEF_VAR) %>%
arrange(desc(COEF_VAR))
COEF_VAR vs. CENT
df_comp %>%
filter(SUBSET == 'full') %>%
select(LEMMA, COEF_VAR, CENT_DEGREE) %>%
ggplot(., aes(y=COEF_VAR, x=CENT_DEGREE)) +
geom_text(aes(label=LEMMA)) +
scale_y_continuous(trans='log')
Correlations: EDA
library(Hmisc)
df_corr <- df_comp %>%
# filter(SUBSET == 'last') %>%
select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
# select(-c(USERS, AGE)) %>%
# mutate(FOCUS = USES) %>%
# focus(FOCUS) %>%
# ggplot(., aes(reorder(rowname, FOCUS), FOCUS)) +
# geom_col() +
# coord_flip()
# rearrange() %>%
# shave() %>%
# rplot()
# network_plot(min_cor=.5) %>%
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bCRDRU5UX0RFR1JFRSkKYGBgCgoKIyMgRGVncmVlIGNlbnRyYWxpdHkgdnMuIGNvbW11bml0aWVzCgojIyMgQ29ycmVsYXRpb24KCmBgYHtyfQpkZl9jb21wICU+JQogIGZpbHRlcihTVUJTRVQgPT0gJ2xhc3QnKSAlPiUKICBzZWxlY3QoQ0VOVF9ERUdSRUUsIENPTU1VTklUSUVTKSAlPiUKICBtdXRhdGUoQ09NTVVOSVRJRVMgPSBhcy5udW1lcmljKENPTU1VTklUSUVTKSkgJT4lCiAgY29ycmVsYXRlKCkKYGBgCgoKIyMjIFBsb3QKCmBgYHtyfQpkZl9jb21wICU+JQogIGZpbHRlcihTVUJTRVQgPT0gJ2xhc3QnKSAlPiUKICBzZWxlY3QoTEVNTUEsIENFTlRfREVHUkVFLCBDT01NVU5JVElFUykgJT4lCiAgZ2dwbG90KC4sIGFlcyh4PUNFTlRfREVHUkVFLCB5PWFzLm51bWVyaWMoQ09NTVVOSVRJRVMpKSkgKwogICAgZ2VvbV90ZXh0KGFlcyhsYWJlbD1MRU1NQSkpICsKICAgIHNjYWxlX3hfY29udGludW91cyh0cmFucz0nbG9nJykKYGBgCgoKCiMjIFVzZXMgdnMuIHVzZXJzCgojIyMgUGxvdAoKYGBge3J9CnBsdCA8LSBkZl9jb21wICU+JQogIGZpbHRlcihTVUJTRVQgPT0gJ2Z1bGwnKSAlPiUKICBzZWxlY3QoTEVNTUEsIFVTRVMsIFVTRVJTKSAlPiUKICBnZ3Bsb3QoLiwgYWVzKHg9VVNFUlMsIHk9VVNFUykpICsKICAgIGdlb21fdGV4dChhZXMobGFiZWw9TEVNTUEpKSArCiAgICBzY2FsZV94X2NvbnRpbnVvdXModHJhbnM9J2xvZycpICsKICAgIHNjYWxlX3lfY29udGludW91cyh0cmFucz0nbG9nJykgKwogICAgZ2VvbV9zbW9vdGgobWV0aG9kPWxtKQpnZ3Bsb3RseShwbHQpCmBgYAoKIyMjIENvcnJlbGF0aW9uCgpgYGB7cn0KZGZfY29tcCAlPiUKICBmaWx0ZXIoU1VCU0VUID09ICdmdWxsJykgJT4lCiAgc2VsZWN0KFVTRVMsIFVTRVJTKSAlPiUKICBjb3JyZWxhdGUoKQpgYGAKCgojIyBDb2VmZmljaWVudCBvZiB2YXJpYXRpb24KCmBgYHtyfQpkZl9jb21wICU+JQogIGZpbHRlcigKICAgIFNVQlNFVCA9PSAnZnVsbCcsCiAgICBVU0VTID49IDEwMDAKICAgICkgJT4lCiAgc2VsZWN0KExFTU1BLCBVU0VTLCBDT0VGX1ZBUikgJT4lCiAgYXJyYW5nZShkZXNjKENPRUZfVkFSKSkKYGBgCgoKIyBDT0VGX1ZBUiB2cy4gQ0VOVAoKYGBge3J9CmRmX2NvbXAgJT4lCiAgZmlsdGVyKFNVQlNFVCA9PSAnZnVsbCcpICU+JQogIHNlbGVjdChMRU1NQSwgQ09FRl9WQVIsIENFTlRfREVHUkVFKSAlPiUKICBnZ3Bsb3QoLiwgYWVzKHk9Q09FRl9WQVIsIHg9Q0VOVF9ERUdSRUUpKSArCiAgICBnZW9tX3RleHQoYWVzKGxhYmVsPUxFTU1BKSkgKwogICAgc2NhbGVfeV9jb250aW51b3VzKHRyYW5zPSdsb2cnKQpgYGAKCgojIENvcnJlbGF0aW9uczogRURBCgpgYGB7cn0KbGlicmFyeShIbWlzYykKCmRmX2NvcnIgPC0gZGZfY29tcCAlPiUKICAjIGZpbHRlcihTVUJTRVQgPT0gJ2xhc3QnKSAlPiUKICBzZWxlY3QoLWMoTEVNTUEsIFNVQlNFVCwgTkVUX1dJTkRPV19EQVRFUywgU0tJUCwgU1RBTVAsIE5ST1dTKSkKICAjIHNlbGVjdCgtYyhVU0VSUywgQUdFKSkgJT4lCiAgIyBtdXRhdGUoRk9DVVMgPSBVU0VTKSAlPiUKICAjIGZvY3VzKEZPQ1VTKSAlPiUKICAjIGdncGxvdCguLCBhZXMocmVvcmRlcihyb3duYW1lLCBGT0NVUyksIEZPQ1VTKSkgKwogICAgIyBnZW9tX2NvbCgpICsKICAgICMgY29vcmRfZmxpcCgpCiAgIyByZWFycmFuZ2UoKSAlPiUKICAjIHNoYXZlKCkgJT4lCiAgIyBycGxvdCgpCiAgIyBuZXR3b3JrX3Bsb3QobWluX2Nvcj0uNSkgJT4lCgogIApgYGAKCgogIA==